Data and Knowledge–Driven Approach for Energy Profiling in Smart Context-Aware Buildings

Farrag, Mona and Feldman, Gerald and Mahmoud, Haitham and Elmitwally, Nouh and Gaber, Mohamed Medhat (2025) Data and Knowledge–Driven Approach for Energy Profiling in Smart Context-Aware Buildings. In: 45th SGAI International Conference on Artificial Intelligence, 16th-18th December 2025, Cambridge, UK.

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Abstract

Energy profiling plays a crucial role in optimising smart building operations, especially with the increasing popularity of personalised, user-centric AI applications. Current research lacks emphasis on interpretability, transparency, and accessibility for non-expert stakeholders, where decision-making either relies solely on machine learning insights or unstructured knowledge bases. Hence, this study aims to enhance the interpretability of energy profiling and generate tailored recommendations based on correlated data sources from various aspects. This approach combines data-driven and knowledge-driven techniques by integrating energy clustering insights and unstructured knowledge bases to provide tailored energy recommendations. By combining Large Language Models (LLMs) and Explainable AI (XAI), this approach leads to: (1) identifying new consumer personas based on contextualised cluster insights, (2) finding the most impactful features reflecting energy insights, and (3) turning those insights into clear, human-readable reports and recommendations. This transforms smart meters from passive data collectors into intelligent advisory tools for consumers, policymakers, and energy providers.

Item Type:	Conference or Workshop Item (Paper)
Identification Number:	10.1007/978-3-032-11402-0_6
Dates:	Date Event 1 November 2025 Accepted 24 November 2025 Published Online
Uncontrolled Keywords:	Smart building, Energy profiling, Smart Meter Analysis, Large Language Models, Explainable AI
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	04 Dec 2025 15:40
Last Modified:	04 Dec 2025 15:40
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16764

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